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Electronics
Special Issues
Emerging Technologies in Digital Twins
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Emerging Technologies in Digital Twins

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 633

Special Issue Editor

Prof. Dr. Alessandro Massaro

E-Mail Website
Guest Editor
LUM Enterprise, LUM University, S.S. 100-km 18, Casamassima, 70010 Bari, Italy
Interests: Industry 4.0 and 5.0; human activity recognition; artificial intelligence; optoelectronic sensors; sensors; electronics; IoT; cybersecurity

Special Issue Information

Dear Colleagues,

I would like to propose a new Special Issue in Electronics.

This Special Issue will centre around the advances in electronic engineering in relation to optoelectronics, Industry 4.0, Industry 5.0, industrial IoT, and measurement protocols. Particular attention will be paid to the use of artificial intelligence in mechatronic control, industrial processes, predictive maintenance, product quality assessment, security systems, environment monitoring, pollution monitoring, material control, and micro/nano-sensing and actuation. The aim is to study the listed emerging technologies in digital twin applications. 

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Digital twin in manufacturing;
  • Pollution monitoring and prediction;
  • Environmental parameters modelling;
  • Industry 4.0;
  • Industry 5.0;
  • Intelligent electronics;
  • Predictive maintenance;
  • Quality assessment and digital twin,
  • Innovative material control;
  • Digital twin in complex energy systems.

I look forward to receiving your contributions.

Prof. Dr. Alessandro Massaro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital twin
  • intelligent electronics
  • Industry 4.0/5.0
  • miniaturized sensors
  • micro and nano technologies
  • environmental monitoring
  • production prediction innovative materials in electronics
  • IoT
  • measurement protocols

Published Papers (1 paper)

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Research

17 pages, 5550 KiB  
Article
Synthetic Dataset Generation Using Photo-Realistic Simulation with Varied Time and Weather Axes
by Thomas Lee, Susan Mckeever and Jane Courtney
Electronics 2024, 13(8), 1516; https://doi.org/10.3390/electronics13081516 - 17 Apr 2024
Viewed by 425
Abstract
To facilitate the integration of autonomous unmanned air vehicles (UAVs) in day-to-day life, it is imperative that safe navigation can be demonstrated in all relevant scenarios. For UAVs using a navigational protocol driven by artificial neural networks, training and testing data from multiple [...] Read more.
To facilitate the integration of autonomous unmanned air vehicles (UAVs) in day-to-day life, it is imperative that safe navigation can be demonstrated in all relevant scenarios. For UAVs using a navigational protocol driven by artificial neural networks, training and testing data from multiple environmental contexts are needed to ensure that bias is minimised. The reduction in predictive capacity when faced with unfamiliar data is a common weak point in trained networks, which worsens the further the input data deviates from the training data. However, training for multiple environmental variables dramatically increases the man-hours required for data collection and validation. In this work, a potential solution to this data availability issue is presented through the generation and evaluation of photo-realistic image datasets from a simulation of 3D-scanned physical spaces which are theoretically linked in a digital twin (DT) configuration. This simulation is then used to generate environmentally varied iterations of the target object in that physical space by two contextual variables (weather and daylight). This results in an expanded dataset of bicycles that contains weather and time-varied components of the same images which are then evaluated using a generic build of the YoloV3 object detection network; the response is then compared to two real image (night and day) datasets as a baseline. The results reveal that the network response remained consistent across the temporal axis, maintaining a measured domain shift of approximately 23% between the two baselines. Full article
(This article belongs to the Special Issue Emerging Technologies in Digital Twins)
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